File size: 2,717 Bytes
1bdd341 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
language: es
thumbnail: https://i.imgur.com/jgBdimh.png
---
# Spanish BERT (BETO) + NER
This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) version of the Spanish BERT cased [(BETO)](https://github.com/dccuchile/beto) for **NER** downstream task.
## Details of the downstream task (NER) - Dataset
- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
I preprocessed the dataset and split it as train / dev (80/20)
| Dataset | # Examples |
| ---------------------- | ----- |
| Train | 8.7 K |
| Dev | 2.2 K |
- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py)
- Labels covered:
```
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
```
## Metrics on evaluation set:
| Metric | # score |
| :------------------------------------------------------------------------------------: | :-------: |
| F1 | **90.17**
| Precision | **89.86** |
| Recall | **90.47** |
## Comparison:
| Model | # F1 score |Size(MB)|
| :--------------------------------------------------------------------------------------------------------------: | :-------: |:------|
| bert-base-spanish-wwm-cased (BETO) | 88.43 | 421
| [bert-spanish-cased-finetuned-ner (this one)](https://huggingface.co/mrm8488/bert-spanish-cased-finetuned-ner) | **90.17** | 420 |
| Best Multilingual BERT | 87.38 | 681 |
|[TinyBERT-spanish-uncased-finetuned-ner](https://huggingface.co/mrm8488/TinyBERT-spanish-uncased-finetuned-ner) | 70.00 | **55** |
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
nlp_ner = pipeline(
"ner",
model="mrm8488/bert-spanish-cased-finetuned-ner",
tokenizer=(
'mrm8488/bert-spanish-cased-finetuned-ner',
{"use_fast": False}
))
text = 'Mis amigos están pensando viajar a Londres este verano'
nlp_ner(text)
#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|